Overview

Dataset statistics

Number of variables25
Number of observations400
Missing cells1012
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.2 KiB
Average record size in memory208.0 B

Variable types

Numeric13
Categorical7
Boolean5

Alerts

al is highly overall correlated with sc and 7 other fieldsHigh correlation
su is highly overall correlated with dmHigh correlation
bu is highly overall correlated with rbc and 1 other fieldsHigh correlation
sc is highly overall correlated with al and 8 other fieldsHigh correlation
sod is highly overall correlated with al and 1 other fieldsHigh correlation
hemo is highly overall correlated with ane and 1 other fieldsHigh correlation
pcv is highly overall correlated with al and 8 other fieldsHigh correlation
rbcc is highly overall correlated with al and 8 other fieldsHigh correlation
sg is highly overall correlated with classHigh correlation
rbc is highly overall correlated with al and 4 other fieldsHigh correlation
pc is highly overall correlated with al and 4 other fieldsHigh correlation
pcc is highly overall correlated with pcHigh correlation
htn is highly overall correlated with al and 5 other fieldsHigh correlation
dm is highly overall correlated with su and 5 other fieldsHigh correlation
cad is highly overall correlated with rbccHigh correlation
ane is highly overall correlated with bu and 4 other fieldsHigh correlation
class is highly overall correlated with al and 9 other fieldsHigh correlation
pcc is highly imbalanced (51.2%)Imbalance
ba is highly imbalanced (69.0%)Imbalance
cad is highly imbalanced (57.9%)Imbalance
age has 9 (2.2%) missing valuesMissing
bp has 12 (3.0%) missing valuesMissing
sg has 47 (11.8%) missing valuesMissing
al has 46 (11.5%) missing valuesMissing
su has 49 (12.2%) missing valuesMissing
rbc has 152 (38.0%) missing valuesMissing
pc has 65 (16.2%) missing valuesMissing
bgr has 44 (11.0%) missing valuesMissing
bu has 19 (4.8%) missing valuesMissing
sc has 17 (4.2%) missing valuesMissing
sod has 87 (21.8%) missing valuesMissing
pot has 88 (22.0%) missing valuesMissing
hemo has 52 (13.0%) missing valuesMissing
pcv has 71 (17.8%) missing valuesMissing
wbcc has 106 (26.5%) missing valuesMissing
rbcc has 131 (32.8%) missing valuesMissing
al has 199 (49.8%) zerosZeros
su has 290 (72.5%) zerosZeros

Reproduction

Analysis started2024-08-12 23:55:28.803966
Analysis finished2024-08-12 23:56:18.847178
Duration50.04 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

MISSING 

Distinct76
Distinct (%)19.4%
Missing9
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean51.483376
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:18.900710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q142
median55
Q364.5
95-th percentile74.5
Maximum90
Range88
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation17.169714
Coefficient of variation (CV)0.33350016
Kurtosis0.057840495
Mean51.483376
Median Absolute Deviation (MAD)10
Skewness-0.66825947
Sum20130
Variance294.79908
MonotonicityNot monotonic
2024-08-13T07:56:18.980288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 19
 
4.8%
65 17
 
4.2%
48 12
 
3.0%
50 12
 
3.0%
55 12
 
3.0%
47 11
 
2.8%
56 10
 
2.5%
59 10
 
2.5%
45 10
 
2.5%
54 10
 
2.5%
Other values (66) 268
67.0%
ValueCountFrequency (%)
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 2
0.5%
6 1
 
0.2%
7 1
 
0.2%
8 3
0.8%
11 1
 
0.2%
12 2
0.5%
14 1
 
0.2%
ValueCountFrequency (%)
90 1
 
0.2%
83 1
 
0.2%
82 1
 
0.2%
81 1
 
0.2%
80 4
1.0%
79 1
 
0.2%
78 1
 
0.2%
76 5
1.2%
75 5
1.2%
74 3
0.8%

bp
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)2.6%
Missing12
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean76.469072
Minimum50
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:19.050387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median80
Q380
95-th percentile100
Maximum180
Range130
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.683637
Coefficient of variation (CV)0.17894342
Kurtosis8.6460952
Mean76.469072
Median Absolute Deviation (MAD)10
Skewness1.605429
Sum29670
Variance187.24194
MonotonicityNot monotonic
2024-08-13T07:56:19.109926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
80 116
29.0%
70 112
28.0%
60 71
17.8%
90 53
13.2%
100 25
 
6.2%
50 5
 
1.2%
110 3
 
0.8%
140 1
 
0.2%
180 1
 
0.2%
120 1
 
0.2%
(Missing) 12
 
3.0%
ValueCountFrequency (%)
50 5
 
1.2%
60 71
17.8%
70 112
28.0%
80 116
29.0%
90 53
13.2%
100 25
 
6.2%
110 3
 
0.8%
120 1
 
0.2%
140 1
 
0.2%
180 1
 
0.2%
ValueCountFrequency (%)
180 1
 
0.2%
140 1
 
0.2%
120 1
 
0.2%
110 3
 
0.8%
100 25
 
6.2%
90 53
13.2%
80 116
29.0%
70 112
28.0%
60 71
17.8%
50 5
 
1.2%

sg
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)1.4%
Missing47
Missing (%)11.8%
Memory size6.2 KiB
1.02
106 
1.01
84 
1.025
81 
1.015
75 
1.005
 
7

Length

Max length5
Median length4
Mean length4.4617564
Min length4

Characters and Unicode

Total characters1575
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.02
2nd row1.02
3rd row1.01
4th row1.005
5th row1.01

Common Values

ValueCountFrequency (%)
1.02 106
26.5%
1.01 84
21.0%
1.025 81
20.2%
1.015 75
18.8%
1.005 7
 
1.8%
(Missing) 47
11.8%

Length

2024-08-13T07:56:19.191429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T07:56:19.276064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02 106
30.0%
1.01 84
23.8%
1.025 81
22.9%
1.015 75
21.2%
1.005 7
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1222
77.6%
Other Punctuation 353
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 512
41.9%
0 360
29.5%
2 187
 
15.3%
5 163
 
13.3%
Other Punctuation
ValueCountFrequency (%)
. 353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

al
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)1.7%
Missing46
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean1.0169492
Minimum0
Maximum5
Zeros199
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:19.341966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3526789
Coefficient of variation (CV)1.3301343
Kurtosis-0.3833766
Mean1.0169492
Median Absolute Deviation (MAD)0
Skewness0.99815724
Sum360
Variance1.8297402
MonotonicityNot monotonic
2024-08-13T07:56:19.405611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 199
49.8%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
(Missing) 46
 
11.5%
ValueCountFrequency (%)
0 199
49.8%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 24
 
6.0%
3 43
 
10.8%
2 43
 
10.8%
1 44
 
11.0%
0 199
49.8%

su
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)1.7%
Missing49
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean0.45014245
Minimum0
Maximum5
Zeros290
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:19.470165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0991913
Coefficient of variation (CV)2.4418742
Kurtosis5.055348
Mean0.45014245
Median Absolute Deviation (MAD)0
Skewness2.4642618
Sum158
Variance1.2082214
MonotonicityNot monotonic
2024-08-13T07:56:19.533165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 290
72.5%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
1 13
 
3.2%
5 3
 
0.8%
(Missing) 49
 
12.2%
ValueCountFrequency (%)
0 290
72.5%
1 13
 
3.2%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
5 3
 
0.8%
ValueCountFrequency (%)
5 3
 
0.8%
4 13
 
3.2%
3 14
 
3.5%
2 18
 
4.5%
1 13
 
3.2%
0 290
72.5%

rbc
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.8%
Missing152
Missing (%)38.0%
Memory size6.2 KiB
normal
201 
abnormal
47 

Length

Max length8
Median length6
Mean length6.3790323
Min length6

Characters and Unicode

Total characters1582
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 201
50.2%
abnormal 47
 
11.8%
(Missing) 152
38.0%

Length

2024-08-13T07:56:19.613701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T07:56:19.685336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal 201
81.0%
abnormal 47
 
19.0%

Most occurring characters

ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1582
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1582
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

pc
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.6%
Missing65
Missing (%)16.2%
Memory size6.2 KiB
normal
259 
abnormal
76 

Length

Max length8
Median length6
Mean length6.4537313
Min length6

Characters and Unicode

Total characters2162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rowabnormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 259
64.8%
abnormal 76
 
19.0%
(Missing) 65
 
16.2%

Length

2024-08-13T07:56:19.753246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T07:56:19.838826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal 259
77.3%
abnormal 76
 
22.7%

Most occurring characters

ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2162
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

pcc
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size6.2 KiB
notpresent
354 
present
42 

Length

Max length10
Median length10
Mean length9.6818182
Min length7

Characters and Unicode

Total characters3834
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rowpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 354
88.5%
present 42
 
10.5%
(Missing) 4
 
1.0%

Length

2024-08-13T07:56:20.030845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T07:56:20.102484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 354
89.4%
present 42
 
10.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3834
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 3834
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

ba
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size6.2 KiB
notpresent
374 
present
 
22

Length

Max length10
Median length10
Mean length9.8333333
Min length7

Characters and Unicode

Total characters3894
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rownotpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 374
93.5%
present 22
 
5.5%
(Missing) 4
 
1.0%

Length

2024-08-13T07:56:20.180189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T07:56:20.250293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 374
94.4%
present 22
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3894
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 3894
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

bgr
Real number (ℝ)

MISSING 

Distinct146
Distinct (%)41.0%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean148.03652
Minimum22
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:20.317773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78.75
Q199
median121
Q3163
95-th percentile307.25
Maximum490
Range468
Interquartile range (IQR)64

Descriptive statistics

Standard deviation79.281714
Coefficient of variation (CV)0.53555512
Kurtosis4.2255936
Mean148.03652
Median Absolute Deviation (MAD)25
Skewness2.0107732
Sum52701
Variance6285.5902
MonotonicityNot monotonic
2024-08-13T07:56:20.404571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 10
 
2.5%
93 9
 
2.2%
100 9
 
2.2%
107 8
 
2.0%
131 6
 
1.5%
140 6
 
1.5%
109 6
 
1.5%
92 6
 
1.5%
117 6
 
1.5%
130 6
 
1.5%
Other values (136) 284
71.0%
(Missing) 44
 
11.0%
ValueCountFrequency (%)
22 1
 
0.2%
70 5
1.2%
74 3
0.8%
75 2
 
0.5%
76 4
1.0%
78 3
0.8%
79 3
0.8%
80 2
 
0.5%
81 3
0.8%
82 3
0.8%
ValueCountFrequency (%)
490 2
0.5%
463 1
0.2%
447 1
0.2%
425 1
0.2%
424 2
0.5%
423 1
0.2%
415 1
0.2%
410 1
0.2%
380 1
0.2%
360 2
0.5%

bu
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct118
Distinct (%)31.0%
Missing19
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean57.425722
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:20.488248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median42
Q366
95-th percentile162
Maximum391
Range389.5
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.503006
Coefficient of variation (CV)0.87944921
Kurtosis9.3452886
Mean57.425722
Median Absolute Deviation (MAD)16
Skewness2.6343745
Sum21879.2
Variance2550.5536
MonotonicityNot monotonic
2024-08-13T07:56:20.576876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 15
 
3.8%
25 13
 
3.2%
19 11
 
2.8%
40 10
 
2.5%
15 9
 
2.2%
48 9
 
2.2%
50 9
 
2.2%
18 9
 
2.2%
32 8
 
2.0%
49 8
 
2.0%
Other values (108) 280
70.0%
(Missing) 19
 
4.8%
ValueCountFrequency (%)
1.5 1
 
0.2%
10 2
 
0.5%
15 9
2.2%
16 7
1.8%
17 7
1.8%
18 9
2.2%
19 11
2.8%
20 7
1.8%
21 1
 
0.2%
22 6
1.5%
ValueCountFrequency (%)
391 1
0.2%
322 1
0.2%
309 1
0.2%
241 1
0.2%
235 1
0.2%
223 1
0.2%
219 1
0.2%
217 1
0.2%
215 1
0.2%
208 1
0.2%

sc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)21.9%
Missing17
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.0724543
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:20.673398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.3
Q32.8
95-th percentile11.89
Maximum76
Range75.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation5.7411261
Coefficient of variation (CV)1.8685798
Kurtosis79.304345
Mean3.0724543
Median Absolute Deviation (MAD)0.6
Skewness7.5095383
Sum1176.75
Variance32.960529
MonotonicityNot monotonic
2024-08-13T07:56:20.756500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 40
 
10.0%
1.1 24
 
6.0%
0.5 23
 
5.8%
1 23
 
5.8%
0.9 22
 
5.5%
0.7 22
 
5.5%
0.6 18
 
4.5%
0.8 17
 
4.2%
2.2 10
 
2.5%
1.5 9
 
2.2%
Other values (74) 175
43.8%
(Missing) 17
 
4.2%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.5 23
5.8%
0.6 18
4.5%
0.7 22
5.5%
0.8 17
4.2%
0.9 22
5.5%
1 23
5.8%
1.1 24
6.0%
1.2 40
10.0%
1.3 8
 
2.0%
ValueCountFrequency (%)
76 1
0.2%
48.1 1
0.2%
32 1
0.2%
24 1
0.2%
18.1 1
0.2%
18 1
0.2%
16.9 1
0.2%
16.4 1
0.2%
15.2 1
0.2%
15 1
0.2%

sod
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)10.9%
Missing87
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean137.52875
Minimum4.5
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:20.837903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile125
Q1135
median138
Q3142
95-th percentile150
Maximum163
Range158.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.408752
Coefficient of variation (CV)0.075684188
Kurtosis85.53437
Mean137.52875
Median Absolute Deviation (MAD)3
Skewness-6.9965686
Sum43046.5
Variance108.34212
MonotonicityNot monotonic
2024-08-13T07:56:20.919536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
135 40
10.0%
140 25
 
6.2%
141 22
 
5.5%
139 21
 
5.2%
142 20
 
5.0%
138 20
 
5.0%
137 19
 
4.8%
150 17
 
4.2%
136 17
 
4.2%
147 13
 
3.2%
Other values (24) 99
24.8%
(Missing) 87
21.8%
ValueCountFrequency (%)
4.5 1
 
0.2%
104 1
 
0.2%
111 1
 
0.2%
113 2
0.5%
114 2
0.5%
115 1
 
0.2%
120 2
0.5%
122 2
0.5%
124 3
0.8%
125 2
0.5%
ValueCountFrequency (%)
163 1
 
0.2%
150 17
4.2%
147 13
3.2%
146 10
 
2.5%
145 11
2.8%
144 9
 
2.2%
143 4
 
1.0%
142 20
5.0%
141 22
5.5%
140 25
6.2%

pot
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)12.8%
Missing88
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean4.6272436
Minimum2.5
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:21.004160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.4
Q13.8
median4.4
Q34.9
95-th percentile5.7
Maximum47
Range44.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation3.1939042
Coefficient of variation (CV)0.69023904
Kurtosis142.50591
Mean4.6272436
Median Absolute Deviation (MAD)0.5
Skewness11.582956
Sum1443.7
Variance10.201024
MonotonicityNot monotonic
2024-08-13T07:56:21.086582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.5 30
 
7.5%
5 30
 
7.5%
4.9 27
 
6.8%
4.7 17
 
4.2%
4.8 16
 
4.0%
4 14
 
3.5%
4.1 14
 
3.5%
4.4 14
 
3.5%
3.9 14
 
3.5%
3.8 14
 
3.5%
Other values (30) 122
30.5%
(Missing) 88
22.0%
ValueCountFrequency (%)
2.5 2
 
0.5%
2.7 1
 
0.2%
2.8 1
 
0.2%
2.9 3
 
0.8%
3 2
 
0.5%
3.2 3
 
0.8%
3.3 3
 
0.8%
3.4 5
 
1.2%
3.5 30
7.5%
3.6 8
 
2.0%
ValueCountFrequency (%)
47 1
 
0.2%
39 1
 
0.2%
7.6 1
 
0.2%
6.6 1
 
0.2%
6.5 2
0.5%
6.4 1
 
0.2%
6.3 3
0.8%
5.9 2
0.5%
5.8 2
0.5%
5.7 4
1.0%

hemo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)33.0%
Missing52
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12.526437
Minimum3.1
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:21.169252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile7.9
Q110.3
median12.65
Q315
95-th percentile16.9
Maximum17.8
Range14.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.9125866
Coefficient of variation (CV)0.23251517
Kurtosis-0.47139804
Mean12.526437
Median Absolute Deviation (MAD)2.35
Skewness-0.33509468
Sum4359.2
Variance8.4831608
MonotonicityNot monotonic
2024-08-13T07:56:21.253215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 16
 
4.0%
10.9 8
 
2.0%
13.6 7
 
1.8%
13 7
 
1.8%
9.8 7
 
1.8%
11.1 7
 
1.8%
10.3 6
 
1.5%
11.3 6
 
1.5%
13.9 6
 
1.5%
12 6
 
1.5%
Other values (105) 272
68.0%
(Missing) 52
 
13.0%
ValueCountFrequency (%)
3.1 1
0.2%
4.8 1
0.2%
5.5 1
0.2%
5.6 1
0.2%
5.8 1
0.2%
6 2
0.5%
6.1 1
0.2%
6.2 1
0.2%
6.3 1
0.2%
6.6 1
0.2%
ValueCountFrequency (%)
17.8 3
0.8%
17.7 1
 
0.2%
17.6 1
 
0.2%
17.5 1
 
0.2%
17.4 2
0.5%
17.3 1
 
0.2%
17.2 2
0.5%
17.1 2
0.5%
17 4
1.0%
16.9 2
0.5%

pcv
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)12.8%
Missing71
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean38.884498
Minimum9
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:21.346557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24
Q132
median40
Q345
95-th percentile52
Maximum54
Range45
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.9901048
Coefficient of variation (CV)0.23120023
Kurtosis-0.32056168
Mean38.884498
Median Absolute Deviation (MAD)7
Skewness-0.4336786
Sum12793
Variance80.821985
MonotonicityNot monotonic
2024-08-13T07:56:21.428123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
41 21
 
5.2%
52 21
 
5.2%
44 19
 
4.8%
48 19
 
4.8%
40 16
 
4.0%
43 15
 
3.8%
45 13
 
3.2%
42 13
 
3.2%
36 12
 
3.0%
33 12
 
3.0%
Other values (32) 168
42.0%
(Missing) 71
17.8%
ValueCountFrequency (%)
9 1
 
0.2%
14 1
 
0.2%
15 1
 
0.2%
16 1
 
0.2%
17 1
 
0.2%
18 1
 
0.2%
19 2
0.5%
20 1
 
0.2%
21 1
 
0.2%
22 3
0.8%
ValueCountFrequency (%)
54 4
 
1.0%
53 4
 
1.0%
52 21
5.2%
51 4
 
1.0%
50 12
3.0%
49 4
 
1.0%
48 19
4.8%
47 4
 
1.0%
46 9
2.2%
45 13
3.2%

wbcc
Real number (ℝ)

MISSING 

Distinct89
Distinct (%)30.3%
Missing106
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean8406.1224
Minimum2200
Maximum26400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:21.501659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2200
5-th percentile4500
Q16500
median8000
Q39800
95-th percentile12940
Maximum26400
Range24200
Interquartile range (IQR)3300

Descriptive statistics

Standard deviation2944.4742
Coefficient of variation (CV)0.35027734
Kurtosis6.1506398
Mean8406.1224
Median Absolute Deviation (MAD)1700
Skewness1.6215894
Sum2471400
Variance8669928.3
MonotonicityNot monotonic
2024-08-13T07:56:21.586423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9800 11
 
2.8%
6700 10
 
2.5%
9600 9
 
2.2%
7200 9
 
2.2%
9200 9
 
2.2%
6900 8
 
2.0%
5800 8
 
2.0%
11000 8
 
2.0%
7800 7
 
1.8%
7000 7
 
1.8%
Other values (79) 208
52.0%
(Missing) 106
26.5%
ValueCountFrequency (%)
2200 1
 
0.2%
2600 1
 
0.2%
3800 2
 
0.5%
4100 1
 
0.2%
4200 3
0.8%
4300 6
1.5%
4500 3
0.8%
4700 4
1.0%
4900 1
 
0.2%
5000 5
1.2%
ValueCountFrequency (%)
26400 1
0.2%
21600 1
0.2%
19100 1
0.2%
18900 1
0.2%
16700 1
0.2%
16300 1
0.2%
15700 1
0.2%
15200 2
0.5%
14900 1
0.2%
14600 2
0.5%

rbcc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)16.7%
Missing131
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean4.7074349
Minimum2.1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-08-13T07:56:21.673126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile2.94
Q13.9
median4.8
Q35.4
95-th percentile6.3
Maximum8
Range5.9
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.0253233
Coefficient of variation (CV)0.21780933
Kurtosis-0.27004248
Mean4.7074349
Median Absolute Deviation (MAD)0.7
Skewness-0.18332932
Sum1266.3
Variance1.0512878
MonotonicityNot monotonic
2024-08-13T07:56:21.756132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5.2 18
 
4.5%
4.5 16
 
4.0%
4.9 14
 
3.5%
4.7 11
 
2.8%
3.9 10
 
2.5%
5 10
 
2.5%
4.8 10
 
2.5%
4.6 9
 
2.2%
3.4 9
 
2.2%
5.9 8
 
2.0%
Other values (35) 154
38.5%
(Missing) 131
32.8%
ValueCountFrequency (%)
2.1 2
0.5%
2.3 1
 
0.2%
2.4 1
 
0.2%
2.5 2
0.5%
2.6 2
0.5%
2.7 2
0.5%
2.8 2
0.5%
2.9 2
0.5%
3 3
0.8%
3.1 2
0.5%
ValueCountFrequency (%)
8 1
 
0.2%
6.5 5
1.2%
6.4 5
1.2%
6.3 4
1.0%
6.2 5
1.2%
6.1 8
2.0%
6 4
1.0%
5.9 8
2.0%
5.8 7
1.8%
5.7 5
1.2%

htn
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size3.9 KiB
False
251 
True
147 
(Missing)
 
2
ValueCountFrequency (%)
False 251
62.7%
True 147
36.8%
(Missing) 2
 
0.5%
2024-08-13T07:56:21.841673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

dm
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size3.9 KiB
False
261 
True
137 
(Missing)
 
2
ValueCountFrequency (%)
False 261
65.2%
True 137
34.2%
(Missing) 2
 
0.5%
2024-08-13T07:56:21.907352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

cad
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size3.9 KiB
False
364 
True
 
34
(Missing)
 
2
ValueCountFrequency (%)
False 364
91.0%
True 34
 
8.5%
(Missing) 2
 
0.5%
2024-08-13T07:56:21.979164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

appet
Categorical

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size6.2 KiB
good
317 
poor
82 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1596
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowgood
3rd rowpoor
4th rowpoor
5th rowgood

Common Values

ValueCountFrequency (%)
good 317
79.2%
poor 82
 
20.5%
(Missing) 1
 
0.2%

Length

2024-08-13T07:56:22.041164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T07:56:22.108723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
good 317
79.4%
poor 82
 
20.6%

Most occurring characters

ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1596
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1596
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

pe
Boolean

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
False
323 
True
76 
(Missing)
 
1
ValueCountFrequency (%)
False 323
80.8%
True 76
 
19.0%
(Missing) 1
 
0.2%
2024-08-13T07:56:22.183266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

ane
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
False
339 
True
60 
(Missing)
 
1
ValueCountFrequency (%)
False 339
84.8%
True 60
 
15.0%
(Missing) 1
 
0.2%
2024-08-13T07:56:22.249244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

class
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
ckd
250 
notckd
150 

Length

Max length6
Median length3
Mean length4.125
Min length3

Characters and Unicode

Total characters1650
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowckd
2nd rowckd
3rd rowckd
4th rowckd
5th rowckd

Common Values

ValueCountFrequency (%)
ckd 250
62.5%
notckd 150
37.5%

Length

2024-08-13T07:56:22.308808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T07:56:22.388296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ckd 250
62.5%
notckd 150
37.5%

Most occurring characters

ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1650
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1650
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Interactions

2024-08-13T07:56:14.918485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:30.175171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:34.346676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:37.156311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:39.766860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:42.340679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:47.542624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:52.335133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:56.460674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:59.489990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:02.816320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:07.530438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:10.701358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:15.184436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:30.547275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:34.568596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:37.383251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:40.005929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:42.765536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:48.040318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:52.657228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:56.720891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:59.758319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:03.199651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:07.796806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:11.163293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:15.429995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:30.716368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:34.638251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:37.442970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:40.068441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:43.048667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:48.268652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:52.829962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:56.827456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:59.871937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:03.410790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:07.905078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:11.346817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:15.539485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:30.877887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:34.700254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:37.498972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:40.123959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:43.302211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:48.458359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:52.983250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:56.919976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:59.970488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:03.615833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:08.002897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:11.535058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:15.649996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:31.052965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:34.757819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:37.553677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:40.174962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:43.552700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:48.647497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:53.145684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:57.017498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:00.085061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:03.818231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:08.100325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:11.713405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:16.076396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:31.697440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:35.188907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:37.952730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:40.575176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:44.273325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:49.176484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:53.778256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:57.409749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:00.618705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:04.355649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:08.519959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:12.189741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:16.402600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:32.126244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:35.539908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:38.247404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:40.872923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:44.802160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:49.774997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:54.216709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:57.773893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:00.992909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:04.954008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:08.992925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:12.610140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:16.660903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:32.465632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:35.812194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:38.464570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:41.093796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:45.241724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:50.178591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:54.568287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:58.059772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:01.273717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:05.363904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:09.274300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:12.947871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:16.836936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:32.679568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:35.950260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:38.588293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:41.218379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:45.529102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:50.441710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:54.818523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:58.335674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:01.452887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:05.643200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:09.444886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:13.307056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:17.015017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:32.919049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:36.104683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:38.726549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:41.358164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:45.962254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:50.735936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:55.076897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:58.507998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:01.643492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:05.934322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:09.621984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:13.542186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:17.483452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:33.358508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:36.576016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:39.055044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:41.670854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:46.493507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:51.212828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:55.504374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:58.849113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:02.013641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:06.401801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:09.994096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:13.984475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:17.665695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:33.598006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:36.720219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:39.326437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:41.930820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:46.820057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:51.504127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:55.750391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:59.035573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:02.203848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:06.818628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:10.188488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:14.259865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:17.969502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:34.113531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:37.000419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:39.615717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:42.198023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:47.236705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:52.044961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:56.225358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:55:59.305943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:02.507482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:07.242838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:10.503591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T07:56:14.643683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-13T07:56:22.461283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
agebpalsubgrbuscsodpothemopcvwbccrbccsgrbcpcpccbahtndmcadappetpeaneclass
age1.0000.0310.2770.2660.0140.2480.349-0.1350.041-0.125-0.307-0.028-0.2950.0000.0000.0000.0000.0000.2730.2850.0530.1730.1920.2090.313
bp0.0311.0000.0100.0930.111-0.0240.106-0.0320.0620.034-0.0110.034-0.0650.1700.3940.2850.1510.1090.3600.2750.0000.2500.1730.2840.459
al0.2770.0101.0000.3580.1140.1170.628-0.5340.043-0.127-0.640-0.089-0.6240.2870.5330.5880.4530.4100.5490.4580.3320.3770.4740.3440.726
su0.2660.0930.3581.0000.1990.1130.343-0.2290.028-0.084-0.2830.043-0.3100.1830.2130.2220.1970.1860.3700.5490.3820.2570.1650.1450.366
bgr0.0140.1110.1140.1991.000-0.0190.003-0.039-0.0680.020-0.0180.009-0.0550.1820.4700.3700.3300.2480.3800.4850.4970.4190.3690.3600.192
bu0.248-0.0240.1170.113-0.0191.0000.1720.0110.050-0.032-0.0910.016-0.1000.2380.5320.4880.4710.4510.4930.3810.4860.3970.3460.5520.282
sc0.3490.1060.6280.3430.0030.1721.000-0.4540.103-0.012-0.691-0.093-0.6360.4120.5720.5420.3920.4330.6240.5290.4570.4680.4400.5720.651
sod-0.135-0.032-0.534-0.229-0.0390.011-0.4541.0000.010-0.0070.4860.1220.4270.4020.4800.4680.3770.3280.4750.4310.3400.3130.3240.3780.535
pot0.0410.0620.0430.028-0.0680.0500.1030.0101.0000.142-0.116-0.035-0.1050.3270.4160.3590.3670.0550.3510.4180.3510.3080.3000.3890.472
hemo-0.1250.034-0.127-0.0840.020-0.032-0.012-0.0070.1421.000-0.004-0.017-0.0030.2610.4710.4790.3130.2100.4910.4270.2910.4350.3230.6140.690
pcv-0.307-0.011-0.640-0.283-0.018-0.091-0.6910.486-0.116-0.0041.0000.1160.7420.2980.5320.5770.3510.2260.6050.5210.3930.4570.4580.6220.774
wbcc-0.0280.034-0.0890.0430.0090.016-0.0930.122-0.035-0.0170.1161.0000.1520.1520.2780.1260.3070.3820.0200.0930.0000.2440.2300.2860.000
rbcc-0.295-0.065-0.624-0.310-0.055-0.100-0.6360.427-0.105-0.0030.7420.1521.0000.3170.4010.5390.3160.2600.6450.5370.5580.4730.4560.6020.726
sg0.0000.1700.2870.1830.1820.2380.4120.4020.3270.2610.2980.1520.3171.0000.4350.3850.2840.2040.4190.4500.1580.2740.3520.2490.789
rbc0.0000.3940.5330.2130.4700.5320.5720.4800.4160.4710.5320.2780.4010.4351.0000.4100.0690.1480.2890.3210.1610.2620.2820.1630.542
pc0.0000.2850.5880.2220.3700.4880.5420.4680.3590.4790.5770.1260.5390.3850.4101.0000.5010.3110.3720.2890.1940.3030.4030.3150.452
pcc0.0000.1510.4530.1970.3300.4710.3920.3770.3670.3130.3510.3070.3160.2840.0690.5011.0000.2520.1770.1450.1650.1710.0770.1550.250
ba0.0000.1090.4100.1860.2480.4510.4330.3280.0550.2100.2260.3820.2600.2040.1480.3110.2521.0000.0560.0430.1330.1250.1080.0000.167
htn0.2730.3600.5490.3700.3800.4930.6240.4750.3510.4910.6050.0200.6450.4190.2890.3720.1770.0561.0000.6000.3120.3330.3600.3360.582
dm0.2850.2750.4580.5490.4850.3810.5290.4310.4180.4270.5210.0930.5370.4500.3210.2890.1450.0430.6001.0000.2560.3130.2960.1670.550
cad0.0530.0000.3320.3820.4970.4860.4570.3400.3510.2910.3930.0000.5580.1580.1610.1940.1650.1330.3120.2561.0000.1350.1520.0000.220
appet0.1730.2500.3770.2570.4190.3970.4680.3130.3080.4350.4570.2440.4730.2740.2620.3030.1710.1250.3330.3130.1351.0000.4060.2410.383
pe0.1920.1730.4740.1650.3690.3460.4400.3240.3000.3230.4580.2300.4560.3520.2820.4030.0770.1080.3600.2960.1520.4061.0000.1910.365
ane0.2090.2840.3440.1450.3600.5520.5720.3780.3890.6140.6220.2860.6020.2490.1630.3150.1550.0000.3360.1670.0000.2410.1911.0000.314
class0.3130.4590.7260.3660.1920.2820.6510.5350.4720.6900.7740.0000.7260.7890.5420.4520.2500.1670.5820.5500.2200.3830.3650.3141.000

Missing values

2024-08-13T07:56:18.234695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-13T07:56:18.454003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-13T07:56:18.671637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agebpsgalsurbcpcpccbabgrbuscsodpothemopcvwbccrbcchtndmcadappetpeaneclass
048801.0210NaNnormalnotpresentnotpresent121361.2NaNNaN15.44478005.2yesyesnogoodnonockd
17501.0240NaNnormalnotpresentnotpresentNaN180.8NaNNaN11.3386000NaNnononogoodnonockd
262801.0123normalnormalnotpresentnotpresent423531.8NaNNaN9.6317500NaNnoyesnopoornoyesckd
348701.00540normalabnormalpresentnotpresent117563.81112.511.23267003.9yesnonopooryesyesckd
451801.0120normalnormalnotpresentnotpresent106261.4NaNNaN11.63573004.6nononogoodnonockd
560901.01530NaNNaNnotpresentnotpresent74251.11423.212.23978004.4yesyesnogoodyesnockd
668701.0100NaNnormalnotpresentnotpresent1005424104412.436NaNNaNnononogoodnonockd
724NaN1.01524normalabnormalnotpresentnotpresent410311.1NaNNaN12.44469005noyesnogoodyesnockd
8521001.01530normalabnormalpresentnotpresent138601.9NaNNaN10.83396004yesyesnogoodnoyesckd
953901.0220abnormalabnormalpresentnotpresent701077.21143.79.529121003.7yesyesnopoornoyesckd
agebpsgalsurbcpcpccbabgrbuscsodpothemopcvwbccrbcchtndmcadappetpeaneclass
39052801.02500normalnormalnotpresentnotpresent99250.81353.7155263005.3nononogoodnononotckd
39136801.02500normalnormalnotpresentnotpresent85161.11424.115.64458006.3nononogoodnononotckd
39257801.0200normalnormalnotpresentnotpresent133481.21474.314.84666005.5nononogoodnononotckd
39343601.02500normalnormalnotpresentnotpresent117450.71414.4135474005.4nononogoodnononotckd
39450801.0200normalnormalnotpresentnotpresent137460.8139514.14595004.6nononogoodnononotckd
39555801.0200normalnormalnotpresentnotpresent140490.51504.915.74767004.9nononogoodnononotckd
39642701.02500normalnormalnotpresentnotpresent75311.21413.516.55478006.2nononogoodnononotckd
39712801.0200normalnormalnotpresentnotpresent100260.61374.415.84966005.4nononogoodnononotckd
39817601.02500normalnormalnotpresentnotpresent1145011354.914.25172005.9nononogoodnononotckd
39958801.02500normalnormalnotpresentnotpresent131181.11413.515.85368006.1nononogoodnononotckd